Effect of fuzzy PID controller on feedback control systems based on wireless sensor network

Wireless Networked control system (WNCS) has an important in all aspects of the life and in the research fields of Engineering. In this article, a real-time implementation of the wireless feedback control system (WFCS) is performed. The stability issue in the closed-loop control system still suffer from noise, disturbances, and need careful considerations to handle it. Three cases to discover the ability of a Fuzzy PID controller to maintain better angular position control system (PCS) is addressed and controlled by a personal computer through a wireless sensor network(WSN) constructed by ZigBee platforms. The practical issues related with the design and implementation of the wireless computerized control system (WCCS) is discussed and analyzed. The simulation results carried out with Matlab/Simulink 2018b. Different parameters effect such as maximum overshoot, sampling frequency, distance and delay time have been studied. These effects on overall system performance would be discussed. Improving the efficient use of ZigBee platform for WFCS. The simulation and experimental results prove the proposed algorithm in the field of wireless control system

Measurements, Models, Systems and Design

531 s.

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Resilience: A System Interpretation

Resilience has increasingly become a crucial subject to evaluate the function of various real-world systems from ecology, social sciences, and medicine to engineering, critical infrastructure, and the built environment - as our planet and its constituent systems are undergoing a rising trend of perturbations, uncertainty, and change due to natural, human and technological causes. The absence of resilience measures within systems causes the systems not only to deviate from their intended functions under perturbations but also allows the systems themselves to become inefficient and obsolete in the face of the rapidly changing requirements with considerable social, environmental, and economic consequences. Despite its ubiquitous use and practical significance, the term resilience is often poorly and inconsistently used in various disciplines, hindering its universal understanding and application. There is a broad acknowledgment in the literature of a lack of consensus on whether resilience is an inherent system characteristic or a management process. Hence, this thesis adopts a holistic approach giving resilience a system interpretation and argues that much of the resilience literature covers the existing ground in that existing engineering systems stability ideas are being reinvented. The approach used here follows modern control systems theory as the comparison framework, where each system, irrespective of its disciplinary association, is represented in terms of inputs, state, and outputs. Modern control systems theory is adopted because of its cohesiveness and universality. The resilience system interpretation framework defines resilience as adaptive systems and adaptation, where the system has the ability to respond to perturbations and changes through passive and active feedback mechanisms—returning the system state or system form to a starting position or transitioning to another suitable state or form. Various case examples, from plain lumped mass and simple pendulum dynamic systems to, traffic flow and building structure dynamic systems, are utilized to illustrate the resilience system interpretation framework proposed in the thesis. The thesis provides a conceptual cross-disciplinary system framework that offers the potential for a greater understanding of resilience and the elimination of overlap in the literature, particularly as it relates to terminology. In addition, using state-space approaches it quantitively as well as qualitatively evaluates the resilience of cross-disciplinary case systems by utilizing the system's inherent characteristics and management processes. The thesis will be of interest to both academics and practitioners involved in resilience analysis, measurement, and design across various engineering disciplines and by extension any other discipline to enable proactive responses to perturbations while actively adapting to change

A Clustering and SVM Regression Learning-Based Spatiotemporal Fuzzy Logic Controller with Interpretable Structure for Spatially Distributed Systems

Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM) regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design

Advances in System Identification: Gaussian Regression and Robot Inverse Dynamics Learning

Nonparametric Gaussian regression models are powerful tools for supervised learning problems. Recently they have been introduced in the field of system identification as an alternative to classical parametric models used in prediction error methods. The focus of this thesis is the analysis and the extension of linear Gaussian regression models and their applications to the identification of the inverse dynamics of robotic platforms. When Gaussian processes are applied to linear systems identification, according to the Bayesian paradigm the impulse response is modeled a priori with a Gaussian distribution encoding the desired structural properties of the dynamical system (e.g. smoothness, BIBO stability, sparsity, etc.). The inference on the impulse response estimate is obtained through the posterior distribution which combines the information of the a priori distribution together with the information given by the data. The Bayesian framework naturally allows the adaptation of the model class and its complexity while also accounting for uncertainty and noise, thus providing a robust mean to trade bias versus variance. On the other hand, one disadvantage of these nonparametric methods is that their aim to identify directly the impulse response of the predictor model does not guarantee the stability of the forward model. These general advantages and disadvantages inspired the research on this manuscript. A COMPARISON BETWEEN GAUSSIAN REGRESSION AND PARAMETRIC PEM. The term of comparison for these Gaussian regression models will be the classical parametric technique. In addition to an analysis of the two approaches in terms of error in fitting the impulse response estimates, we are interested in comparing the confidence intervals around these estimates. A new definition of the confidence intervals is proposed in order to pave the way for a fair comparison between the two approaches. Numerical simulations show that the Bayesian estimates have higher prediction performance. ONLINE GAUSSIAN REGRESSION. In an on-line system identification setting, new data become available at given time steps and real-time estimation requirements have to be satisfied. The goal is to compute the model estimate with low and fixed computational complexity and a reduced memory storage. We developed a tailored Bayesian procedure which updates the quantities to compute the marginal likelihood and the impulse response estimate iteratively and performs the estimation of the hyperparameters by computing only one iteration of a suitable optimization algorithm to maximize the marginal likelihood. Both quasi-Newton methods and EM algorithm are adopted as optimization algorithms. When time-varying systems are considered, the property of ‘forgetting the past data’ is required. Accordingly we propose two schemes: the usage of a temporal window which slides over the data and the usage of a forgetting factor which is a variable that exponentially decreases the weight of the old data. In particular, we propose to consider the forgetting factor both as a fixed constant or as an estimating variable. The proposed nonparametric procedures have satisfactory performances compared to the batch algorithm and outperform the classical parametric approaches both in terms of computational time and adherence of the model estimate to the true one. ENFORCING MODEL STABILITY IN NONPARAMETRIC GAUSSIAN REGRESSION. The main idea of the Bayesian approach is to frame linear system identification as predictor estimation in an infinite dimensional space with the aid of regularization techniques. This approach is based on the prediction error minimization and can guarantee the identification of stable predictors. Unfortunately, the stability of the predictors does not guarantee the stability of the impulse response of the forward model in general. Various techniques are successfully proposed to guarantee the stability of this model. ONLINE SEMIPARAMETRIC LEARNING FOR INVERSE DYNAMICS MODELING. Dynamic models can be obtained from the first principles of mechanics, using the so called Rigid Body Dynamics. This approach results in a parametric model in which the values of physically meaningful parameters must be provided in order to complete the fixed structure of the model. Alternatively, the nonparametric Gaussian regression modeling can be employed extrapolating the dynamics directly from the experimental data, without making any unrealistic approximation on the physical system (e.g. assuming linear frictions models, ignoring the dynamics of the hydraulic actuators, etc.). Nevertheless, nonparametric models deteriorate their performance when predicting unseen data that are not in the ``neighbourhood'' of the training dataset. In order to exploit the advantages of both techniques, semiparametric models which combine the parametric and the nonparametric models are analyzed

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated